Urgensity of “Halal Tourism”: Religiosity, Awareness, and Interest from Stakeholders

2021 ◽  
Vol 12 (4) ◽  
pp. 968
Author(s):  
Muhammad Ridlo ZARKASYI ◽  
Dhika Amalia KURNIAWAN ◽  
Dio Caisar DARMA
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Decision Makers ◽  
Muslim Countries ◽  
Tourism Sector ◽  
The Relationship ◽  
Survey Approach

The push for the “halal tourism’ attribute in Muslim countries is quite enthusiastic. There are great interest and encouragement from tourists. Explicitly, currently, several regions in Indonesia such as Ponorogo Regency are trying to realize this concept. In this paper, we have the ambition to see how much influence “halal tourism’ has in Ponorogo Regency. Through variable boundaries, the relationship between religiosity, awareness, and interest can be found. This study attempt to reach all stakeholders who are already involved in the tourism sector. The survey approach was carried out by open interviews, where data was collected based on 409 informants. A multiple linear regression model is applied to answer the hypothesis design empirically. As a result, the informants who are divided into 5 groups who have experience in the field of tourism consider the concept of ‘halal tourism’ quite urgent to be revitalized. Other analyzes also found that religiosity and awareness had a significant effect on interest (p <0.05). Awareness plays an important role in the relationship between religiosity and interests. The originality of this invention lies in the elements (variables and items) used, therefore it deserves to be used as a reference at a later date by decision-makers.

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

Canine Parvoviral Enteritis and Its Determinants-An Epidemiological Analysis

2021 ◽  
Author(s):  
M. Geetha ◽  
G. Selvaraju
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Tamil Nadu ◽  
Multiple Linear Regression Model ◽  
Age Group ◽  
Environmental Determinants ◽  
Disease Occurrence ◽  
The Relationship

Background: Canine parvoviral enteritis (CPVE) is a highly contagious disease of dogs of less than two years age group characterized by vomiting, haemorrhagic foul smelling diarrhoea, high grade pyrexia, dehydration and followed by death. The disease is caused by Canine parvovirus type-2 (CPV-2) and its variants, CPV-2a, 2b and 2c. Environmental and host determinants are playing an important role in the occurrence of CPVE in dogs. Limited numbers of research studies have been were conducted on the role of the determinants associated with the disease occurrence. Hence, the present study was aimed to assess the influence of host and environmental determinants associated with the incidence of CPVE in dogs. Methods: Retrospective data on the incidence of CPVE in Namakkal region, Tamil Nadu was collected (2017-2019) from Veterinary Clinical Complex (VCC), Veterinary College and Research Institute (VC and RI), Namakkal, Tamil Nadu and had been subjected to temporal and spatial clustering and regression analysis. One hundred and twenty three faecal samples were collected from dogs with clinical signs of CPVE and subjected to PCR using H primer of CPV. Cross-sectional study was used to investigate the relationship between the disease and hypothesized causal factors. Relative risk, odds ratio were used to determine the causal association. Weather data was collected for the period from 2017-2019 from Animal Feed Analytical and Quality Control Laboratory (AFAQAL), VC and RI, Namakkal to assess the relationship of disease occurrence with the environmental determinants. Multiple linear regression model was developed for prediction of CPVE by correlation of environmental determinants with the occurrence of CPVE. Result: Temporal analysis revealed endemic pattern of CPVE started last week of April, peaks in June and ends in August and second peak was noticed at November month. Higher incidences ( greater than 70%) were noticed in males and less than 6 months age group dogs. Polymerase chain reaction for confirmation of CPV infection in dogs revealed the positivity of 70.73%. Analysis of risk factors associated with CPVE revealed that vaccination, roaming of dogs, maternal vaccination and early weaning having positive statistical association with the incidence of CPVE. Multiple linear regression model revealed that relative humidity is positively associated with the occurrence of CPVE in dogs. Vaccination of dogs against CPV and administration of boosters at regular intervals, weaning of dogs after 45 days of age are used as primary strategies for prevention of CPVE.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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